Linked Open Data (LOD) enable the semantic interoperability of Public Administration (PA) information. Moreover, they allow citizens to reuse public information for creating new services and applications. Although there are many methodologies and guidelines to produce and publish LOD, the PAs still hardly understand and exploit LOD to improve their activities. In this paper we show the use of a set of best practices to support an Italian PA in producing LOD. We show the case of LOD production from existing open datasets related to public services. Together with the production of LOD we present the definition of a reference ontology, the Public Service Ontology, integrated with the datasets. During the application, we highlight and discuss some critical points we found in methodologies and technologies described in the literature, and we identify some potential improvements.
Boselli, R., Cesarini, M., Mercorio, F., Mezzanzanica, M. (2014). Are the methodologies for producing linked open data feasible for public administrations?. In Proceedings of Special Session in Knowledge Discovery meets Information Systems (KomIS) at DATA2014 (pp.399-407). SciTePress [10.5220/0005143303990407].
Are the methodologies for producing linked open data feasible for public administrations?
BOSELLI, ROBERTO;CESARINI, MIRKO;MERCORIO, FABIO;MEZZANZANICA, MARIO
2014
Abstract
Linked Open Data (LOD) enable the semantic interoperability of Public Administration (PA) information. Moreover, they allow citizens to reuse public information for creating new services and applications. Although there are many methodologies and guidelines to produce and publish LOD, the PAs still hardly understand and exploit LOD to improve their activities. In this paper we show the use of a set of best practices to support an Italian PA in producing LOD. We show the case of LOD production from existing open datasets related to public services. Together with the production of LOD we present the definition of a reference ontology, the Public Service Ontology, integrated with the datasets. During the application, we highlight and discuss some critical points we found in methodologies and technologies described in the literature, and we identify some potential improvements.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.